Iris Flower Project¶
The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by Sir Ronald Fisher in the 1936 as an example of discriminant analysis.
The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor), so 150 total samples. Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters.
Here's a picture of the three different Iris types:
In [1]:
# The Iris Setosa
from IPython.display import Image
url = 'http://upload.wikimedia.org/wikipedia/commons/5/56/Kosaciec_szczecinkowaty_Iris_setosa.jpg'
Image(url,width=300, height=300)
Out[1]:
In [2]:
# The Iris Versicolor
from IPython.display import Image
url = 'http://upload.wikimedia.org/wikipedia/commons/4/41/Iris_versicolor_3.jpg'
Image(url,width=300, height=300)
Out[2]:
In [3]:
# The Iris Virginica
from IPython.display import Image
url = 'http://upload.wikimedia.org/wikipedia/commons/9/9f/Iris_virginica.jpg'
Image(url,width=300, height=300)
Out[3]:
The iris dataset contains measurements for 150 iris flowers from three different species.
The three classes in the Iris dataset:
Iris-setosa (n=50)
Iris-versicolor (n=50)
Iris-virginica (n=50)
The four features of the Iris dataset:
sepal length in cm
sepal width in cm
petal length in cm
petal width in cm
Get the data¶
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import seaborn as sns
iris = sns.load_dataset('iris')
Let's visualize the data and get started!
Exploratory Data Analysis¶
In [5]:
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
Create a pairplot of the data set.
In [6]:
sns.pairplot(iris,hue='species',palette='Dark2')
Out[6]:
<seaborn.axisgrid.PairGrid at 0x1b6ea064310>
Setosa is the most separable.
Create a kde plot of sepal_length versus sepal width for setosa species of flower.
In [7]:
setosa = iris[iris['species'] == 'setosa']
sns.scatterplot(x='sepal_width', y='sepal_length', data=setosa)
sns.kdeplot(x='sepal_width', y='sepal_length', data=setosa, cmap="plasma", fill=True)
Out[7]:
<Axes: xlabel='sepal_width', ylabel='sepal_length'>
Train Test Split¶
Split the data into a training set and a testing set.
In [8]:
from sklearn.model_selection import train_test_split
In [9]:
X = iris.drop('species',axis=1)
y = iris['species']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)
Train a Model¶
Now its time to train a Support Vector Machine Classifier.
Call the SVC() model from sklearn and fit the model to the training data.
In [10]:
from sklearn.svm import SVC
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svc_model = SVC()
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svc_model.fit(X_train,y_train)
Out[12]:
SVC()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
SVC()
Model Evaluation¶
Now let's get predictions from the model and create a confusion matrix and a classification report.
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predictions = svc_model.predict(X_test)
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from sklearn.metrics import classification_report,confusion_matrix
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print(confusion_matrix(y_test,predictions))
[[22 0 0] [ 0 12 0] [ 0 0 11]]
In [16]:
print(classification_report(y_test,predictions))
precision recall f1-score support
setosa 1.00 1.00 1.00 22
versicolor 1.00 1.00 1.00 12
virginica 1.00 1.00 1.00 11
accuracy 1.00 45
macro avg 1.00 1.00 1.00 45
weighted avg 1.00 1.00 1.00 45
Our model is pretty good! Let's see if we can tune the parameters to try to get even better.
Let's try using Gridsearch¶
In [17]:
from sklearn.model_selection import GridSearchCV
Create a dictionary called param_grid and fill out some parameters for C and gamma.
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param_grid = {'C': [0.1,1, 10, 100], 'gamma': [1,0.1,0.01,0.001]}
Create a GridSearchCV object and fit it to the training data.
In [19]:
grid = GridSearchCV(SVC(),param_grid,refit=True,verbose=2)
grid.fit(X_train,y_train)
Fitting 5 folds for each of 16 candidates, totalling 80 fits [CV] END .....................................C=0.1, gamma=1; total time= 0.0s [CV] END .....................................C=0.1, gamma=1; total time= 0.0s [CV] END .....................................C=0.1, gamma=1; total time= 0.0s [CV] END .....................................C=0.1, gamma=1; total time= 0.0s [CV] END .....................................C=0.1, gamma=1; total time= 0.0s [CV] END ...................................C=0.1, gamma=0.1; total time= 0.0s [CV] END ...................................C=0.1, gamma=0.1; total time= 0.0s [CV] END ...................................C=0.1, gamma=0.1; total time= 0.0s [CV] END ...................................C=0.1, gamma=0.1; total time= 0.0s [CV] END ...................................C=0.1, gamma=0.1; total time= 0.0s [CV] END ..................................C=0.1, gamma=0.01; total time= 0.0s [CV] END ..................................C=0.1, gamma=0.01; total time= 0.0s [CV] END ..................................C=0.1, gamma=0.01; total time= 0.0s [CV] END ..................................C=0.1, gamma=0.01; total time= 0.0s [CV] END ..................................C=0.1, gamma=0.01; total time= 0.0s [CV] END .................................C=0.1, gamma=0.001; total time= 0.0s [CV] END .................................C=0.1, gamma=0.001; total time= 0.0s [CV] END .................................C=0.1, gamma=0.001; total time= 0.0s [CV] END .................................C=0.1, gamma=0.001; total time= 0.0s [CV] END .................................C=0.1, gamma=0.001; total time= 0.0s [CV] END .......................................C=1, gamma=1; total time= 0.0s [CV] END .......................................C=1, gamma=1; total time= 0.0s [CV] END .......................................C=1, gamma=1; total time= 0.0s [CV] END .......................................C=1, gamma=1; total time= 0.0s [CV] END .......................................C=1, gamma=1; total time= 0.0s [CV] END .....................................C=1, gamma=0.1; total time= 0.0s [CV] END .....................................C=1, gamma=0.1; total time= 0.0s [CV] END .....................................C=1, gamma=0.1; total time= 0.0s [CV] END .....................................C=1, gamma=0.1; total time= 0.0s [CV] END .....................................C=1, gamma=0.1; total time= 0.0s [CV] END ....................................C=1, gamma=0.01; total time= 0.0s [CV] END ....................................C=1, gamma=0.01; total time= 0.0s [CV] END ....................................C=1, gamma=0.01; total time= 0.0s [CV] END ....................................C=1, gamma=0.01; total time= 0.0s [CV] END ....................................C=1, gamma=0.01; total time= 0.0s [CV] END ...................................C=1, gamma=0.001; total time= 0.0s [CV] END ...................................C=1, gamma=0.001; total time= 0.0s [CV] END ...................................C=1, gamma=0.001; total time= 0.0s [CV] END ...................................C=1, gamma=0.001; total time= 0.0s [CV] END ...................................C=1, gamma=0.001; total time= 0.0s [CV] END ......................................C=10, gamma=1; total time= 0.0s [CV] END ......................................C=10, gamma=1; total time= 0.0s [CV] END ......................................C=10, gamma=1; total time= 0.0s [CV] END ......................................C=10, gamma=1; total time= 0.0s [CV] END ......................................C=10, gamma=1; total time= 0.0s [CV] END ....................................C=10, gamma=0.1; total time= 0.0s [CV] END ....................................C=10, gamma=0.1; total time= 0.0s [CV] END ....................................C=10, gamma=0.1; total time= 0.0s [CV] END ....................................C=10, gamma=0.1; total time= 0.0s [CV] END ....................................C=10, gamma=0.1; total time= 0.0s [CV] END ...................................C=10, gamma=0.01; total time= 0.0s [CV] END ...................................C=10, gamma=0.01; total time= 0.0s [CV] END ...................................C=10, gamma=0.01; total time= 0.0s [CV] END ...................................C=10, gamma=0.01; total time= 0.0s [CV] END ...................................C=10, gamma=0.01; total time= 0.0s [CV] END ..................................C=10, gamma=0.001; total time= 0.0s [CV] END ..................................C=10, gamma=0.001; total time= 0.0s [CV] END ..................................C=10, gamma=0.001; total time= 0.0s [CV] END ..................................C=10, gamma=0.001; total time= 0.0s [CV] END ..................................C=10, gamma=0.001; total time= 0.0s [CV] END .....................................C=100, gamma=1; total time= 0.0s [CV] END .....................................C=100, gamma=1; total time= 0.0s [CV] END .....................................C=100, gamma=1; total time= 0.0s [CV] END .....................................C=100, gamma=1; total time= 0.0s [CV] END .....................................C=100, gamma=1; total time= 0.0s [CV] END ...................................C=100, gamma=0.1; total time= 0.0s [CV] END ...................................C=100, gamma=0.1; total time= 0.0s [CV] END ...................................C=100, gamma=0.1; total time= 0.0s [CV] END ...................................C=100, gamma=0.1; total time= 0.0s [CV] END ...................................C=100, gamma=0.1; total time= 0.0s [CV] END ..................................C=100, gamma=0.01; total time= 0.0s [CV] END ..................................C=100, gamma=0.01; total time= 0.0s [CV] END ..................................C=100, gamma=0.01; total time= 0.0s [CV] END ..................................C=100, gamma=0.01; total time= 0.0s [CV] END ..................................C=100, gamma=0.01; total time= 0.0s [CV] END .................................C=100, gamma=0.001; total time= 0.0s [CV] END .................................C=100, gamma=0.001; total time= 0.0s [CV] END .................................C=100, gamma=0.001; total time= 0.0s [CV] END .................................C=100, gamma=0.001; total time= 0.0s [CV] END .................................C=100, gamma=0.001; total time= 0.0s
Out[19]:
GridSearchCV(estimator=SVC(),
param_grid={'C': [0.1, 1, 10, 100],
'gamma': [1, 0.1, 0.01, 0.001]},
verbose=2)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GridSearchCV(estimator=SVC(),
param_grid={'C': [0.1, 1, 10, 100],
'gamma': [1, 0.1, 0.01, 0.001]},
verbose=2)SVC()
SVC()
Now let's take that grid model and create some predictions using the test set and create classification reports and confusion matrices for them.
In [20]:
grid_predictions = grid.predict(X_test)
In [21]:
print(confusion_matrix(y_test,grid_predictions))
[[22 0 0] [ 0 11 1] [ 0 0 11]]
In [22]:
print(classification_report(y_test,grid_predictions))
precision recall f1-score support
setosa 1.00 1.00 1.00 22
versicolor 1.00 0.92 0.96 12
virginica 0.92 1.00 0.96 11
accuracy 0.98 45
macro avg 0.97 0.97 0.97 45
weighted avg 0.98 0.98 0.98 45
There is basically just one point that is too noisey to grab, which makes sense, we don't want to have an overfit model that would be able to grab that.
Export the Notebook as HTML¶
In [ ]:
from nbconvert import HTMLExporter
import nbformat
exporter = HTMLExporter()
notebook_path = 'Iris-flower-notebook.ipynb'
notebook = nbformat.read(notebook_path, as_version=4)
html_output, resources = exporter.from_notebook_node(notebook)
output_file = 'Iris-flower-notebook.html'
with open(output_file, 'w', encoding='utf-8') as f:
f.write(html_output)